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title: Get Started with Additional Resources
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# Get Started with Additional Resources

This section provides an understanding of how to apply best practices and design patterns across Salesforce platform capabilities, including Flows, Apex, and related tools.

## Step-Based Async Framework

## Additional Resources

[Step-Based Aysnc Framework](/docs/architect/fundamentals/guide/step-based-async-framework) is a Processing Framework for running asynchronous jobs with an arbitrary number of steps. This article covers three essential areas that every architect must master:

- **Scheduling and Invocation**: Uses Scheduled Flow and Invocable Apex to orchestrate, trigger, and control execution with environment checks and fault handling.
- **Step Design and Flexibility**: Defines a reusable step interface with support for Apex, Flow, cursor-based processing, and configurable patterns like feature flags and no-op steps.
- **Orchestration and Execution Engine**: Leverages a Step Processor to sequence, execute, retry, and manage steps with centralized logging and lifecycle control.

This article provides s scalable, step-based asynchronous framework for Salesforce that breaks complex processes into modular, independently executable steps with centralized orchestration, logging, and high-volume performance.

## Salesforce Data Synchronization

[Step-Based Aysnc Framework](/docs/architect/fundamentals/guide/data-synchronization) is the core capability part of Salesfore CRM analytics.It is the ecosystem's native business intelligence platform for interactive dashboards and AI-driven insights—facilitates seamless bidirectional data movement with external systems through two core capabilities: SyncOut and SyncIn. CRM Analytics SyncOut pushes Salesforce object data to external platforms, supporting both full and incremental Change Data Capture (CDC) modes alongside built-in hard-delete tracking to maintain absolute data integrity downstream. Completing the loop, CRM Analytics SyncIn pulls external data back into Salesforce objects, leveraging Bulk API 2.0 to reliably and efficiently ingest high-volume datasets for immediate operational use. This document explains how the pattern works, when to use it, and how to get started, using Snowflake as a concrete worked example.

## Salesforce Data Virtualization

[Step-Based Aysnc Framework](/docs/architect/fundamentals/guide/data-virtualization) is an integration architecture pattern that enables Salesforce to query data directly from external systems at runtime, without copying or replicating that data into Salesforce storage.Instead of moving data into Salesforce, it enables Salesforce to query data directly at its source, at runtime, with no replication. Users see live external data through standard Salesforce interfaces. The data never leaves its authoritative home. This document explains how the pattern works, when to use it, and how to get started, using Snowflake as a concrete worked example

## Architectural Patterns for Field Service: Optimizing Contractor Sharing and Visibility at Scale

[Architectural Patterns for Field Service: Optimizing Contractor Sharing and Visibility at Scale](/docs/architect/fundamentals/guide/field-service-contractor-sharing-patterns) compares two architectural patterns for managing external contractor networks in Salesforce Field Service: **territory-based sharing** and **account-based sharing**. This guide covers:

- **Contractor Personas**: Named Contractors, Contractor Companies, and Occasional Workers, and how each persona shapes the appropriate sharing architecture.
- **Territory-Based Sharing**: Using dedicated Service Territories as the primary security boundary, and the scalability trade-offs of this approach at scale.
- **Account-Based Sharing**: Decoupling visibility from geography using criteria-based sharing rules, enabling a unified resource pool across contractors and internal technicians.
- **Decision Framework**: A decision tree and comparison matrix for choosing the right pattern based on competitive overlap and geographical exclusivity.

Helps architects balance platform security with scheduling performance while building a foundation that scales with Agentforce and Data Cloud.